新sEMG数据集覆盖三种前臂姿势，助力更鲁棒的手势识别。
FORS-EMG: A Novel sEMG Dataset for Hand Gesture Recognition Across Multiple Forearm Orientations
- 采集19人12类手势在三种前臂姿态下的多通道肌电信号。
- LDA+SNTDF组合在跨姿态识别中达88.58%最高F1分数。
- 适合假肢控制、人机交互和临床神经研究使用。
表面肌电（sEMG）信号在手势识别与假肢开发中具有重要潜力，但易受前臂姿态、电极位移和肢体位置等生理动态因素影响。现有sEMG数据集普遍缺乏对这些动态因素的考量。本研究提出一种新型多通道sEMG数据集，用于评估12种常见手势在三种前臂姿态（旋后、中立、旋前）下的表现。数据来自19名健全受试者，采用8个MFI EMG电极置于肘部与前臂中部，记录高质量信号。通过信噪比（SNR）与信噪动伪影比（SMR）验证信号质量。采用LDA、SVM、KNN等机器学习分类器及TDD、TSD、FTDD、AR-RMS、SNTDF五种特征提取方法进行分析，并引入1D CNN、RNN、LSTM及混合模型进行深度学习对比。结果显示，基于SNTDF特征的LDA分类器在中立姿态训练、跨所有姿态测试时达到88.58%最高F1分数。全面分析表明该数据集具备成为手势识别技术、临床sEMG研究及人机交互应用基准的潜力。数据集以MATLAB格式公开，可访问：https://www.kaggle.com/datasets/ummerummanchaity/fors-emg-a-novel-semg-dataset。
原文摘要 · Abstract (English)
Surface electromyography (sEMG) signals hold significant potential for gesture recognition and robust prosthetic hand development. However, sEMG signals are affected by various physiological and dynamic factors, including forearm orientation, electrode displacement, and limb position. Most existing sEMG datasets lack these dynamic considerations. This study introduces a novel multichannel sEMG dataset to evaluate commonly used hand gestures across three distinct forearm orientations. The dataset was collected from nineteen able-bodied subjects performing twelve hand gestures in three forearm orientations--supination, rest, and pronation. Eight MFI EMG electrodes were strategically placed at the elbow and mid-forearm to record high-quality EMG signals. Signal quality was validated through Signal-to-Noise Ratio (SNR) and Signal-to-Motion artifact ratio (SMR) metrics. Hand gesture classification performance across forearm orientations was evaluated using machine learning classifiers, including LDA, SVM, and KNN, alongside five feature extraction methods: TDD, TSD, FTDD, AR-RMS, and SNTDF. Furthermore, deep learning models such as 1D CNN, RNN, LSTM, and hybrid architectures were employed for a comprehensive analysis. Notably, the LDA classifier achieved the highest F1 score of 88.58\% with the SNTDF feature set when trained on hand gesture data of resting and tested across gesture data of all orientations. The promising results from extensive analyses underscore the proposed dataset's potential as a benchmark for advancing gesture recognition technologies, clinical sEMG research, and human-computer interaction applications. The dataset is publicly available in MATLAB format. Dataset: \url{https://www.kaggle.com/datasets/ummerummanchaity/fors-emg-a-novel-semg-dataset}
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